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A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

Accepted at LHMP 2020 Workshop (ICRA 2020)
: Hug, Ronny; Becker, Stefan; Hübner, Wolfgang; Arens, Michael

Fulltext urn:nbn:de:0011-n-5959215 (1.9 MByte PDF)
MD5 Fingerprint: e21bd15695befd004b760549f2b741f6
Created on: 21.7.2020

Online im WWW, 2020, 2 pp.
International Conference on Robotics and Automation (ICRA) <2020, Online>
Workshop on Long-term Human Motion Prediction (LHMP) <2, 2020, Online>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informative assembly of a data basis, an approach for determining a dataset representation in terms of a small set of distinguishable prototypical sub-sequences is proposed. The approach employs a sequence alignment followed by a learning vector quantization (LVQ) stage. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.